Dynamic channeling capability is an important feature in elevator systems to enhance system efficiency during up-peak periods. For further background information, note, for example, U.S. Pat. No. 4,846,311 of Kandasamy Thangavelu entitled "Optimized `Up-Peak` Elevator Channeling System with Predicted Traffic Volume Equalized Sector Assignments" of Otis Elevator Company, the assignee hereof, the disclosure of which is incorporated herein by reference, as well as others of assignee's patents. Additionally, note is made of this inventor's application Ser. No. 07/508,312 entitled "Elevator Dynamic Channeling Dispatching for Up-Peak Period" filed on Apr. 12, 1990, Ser. No. 07/508,313 entitled "Elevator Dynamic Channeling Dispatching Optimized Based on Car Capacity" filed on Apr. 12, 1990, and Ser. No. 07/508,318 entitled "Elevator Dynamic Channeling Dispatching Optimized Based on Population Density of the Channel" filed on Apr. 12, 1990.
Dynamic channeling provides a way of balancing the building traffic density evenly among the elevator cars in a building. In channeling generally, the floors above the main floor or lobby are grouped into sectors, with each sector consisting of a set of contiguous floors and with each sector assigned to a car, with such an approach being used during up-peak conditions. For dynamic channeling, rather than merely assigning an equal number of floors to each sector, prediction methodology is used for estimating the future traffic flow levels for the various floors every short time interval, for example, every five (5) minutes based on past events or traffic conditions. These traffic predictors are then used to more intelligently and dynamically assign the floors to more appropriately configured sectors, having possibly varying numbers of floors to optimize the effects of up-peak channeling.
Thus, a modern day, computerized elevator system for an office building continuously monitors and records elevator-related, significant events occurring in the building, preferably for every minute or short interval of the day, at least during the normal business day, and every day of the year, at least for every business day. Based on the data resulting from the building's elevator usage, a series of predictions are performed to estimate the traffic density during the next few upcoming intervals, each of which intervals usually is a relatively short period of time, typically of the order of some few minutes, e.g., as noted above, five (5) minutes.
The predictions used are in turn based on two major factor types--"historic" and "real time" based prediction.
Historic prediction typically is done based on the information collected over the past several days relevant to the same instant or period of time. For example, at 9:15 AM, the historic prediction will predict the traffic arrival count at the lobby for, for example, the next two (2) minute interval consisting of 9:15 AM to 9:17 AM. The prediction is based on the data that was collected and maintained during the same 9:15 AM to 9:17 AM interval on, for example, every regular business day, for the last several days, prior to the day of the prediction.
On the other hand, real time prediction is a prediction based on much more recent data collected over a sufficiently short period of time, usually involving some minutes, to effectively be considered "real time" for the time period for which the prediction is being made. It thus predicts traffic based on the events or data of only the past some minutes, rather than the past few days.
Depending on the number of intervals being "looked ahead" and the type of prediction(s) involved, typically a real time prediction uses a number (one or more) of the past intervals prior to the current interval. For example, at 9:15 AM, the real time prediction might use the data collected during the last three, five (5) minute intervals of, e.g., 9:00 AM to 9:05 AM, 9:05 AM to 9:10 AM, and 9:10 AM to 9:15 AM. Based on these three sets of collected data, the real time prediction predicts the expected traffic for the next five (5) minute interval in a way that matches or at least approximates the current traffic arrival curve.
Single exponential smoothing is preferably used in the historic based predictions, while linear exponential smoothing preferably is used in the real time predictions. These smoothing techniques are discussed in general (but not in any elevator context or in any context analogous thereto) in Forecasting Methods and Applications by Spyros Makridakis and Steven C. Wheelwright (John Wiley & Sons, Inc., 1978), particularly in Section 3.3: "Single Exponential Smoothing" and Section 3.6: "Linear Exponential Smoothing."
A linear combination of these two prediction factors, namely historic (x.sub.h) and real time (x.sub.r), with equal weight being given to the two factors, typically provides the final prediction to be used in having the elevator system initiate or terminate certain elevator dispatching schemes or operations, particularly the initiation and termination of up-peak channeling. This is described in some detail in, for example, the exemplary embodiment of the '311 patent, although variants other than equality of the factors is disclosed in the patent as being possible.
Thus, in accordance with the '311 patent's exemplary embodiment: EQU Final Prediction (X)=ax.sub.h +bx.sub.r
where "a" and "b" are weighing "constants," in which a+b=1 and preferably are equal to each other, namely, a=b=0.5.
This exemplary prediction methodology works perfectly if people keep up the same schedule every day of the week down to the second.
However, in reality, there sometimes will be relatively abnormal variations in people's behavior from day to day, producing passenger traffic shifts. Thus, for example, even though a person or a group of people usually come to work every day at 8:00 AM, some days they are late and some days they are early. This abnormal variance from normal behavior or pattern can produce some out of sync conditions, particularly on the days of the variances, using the previously disclosed, exemplary prediction methodology of the '311 patent, which is based on normal behavior or traffic patterns, which is what exists for most days. Hence, although the '311 patent provided a very substantial advance in the art, it can be further optimized under certain operating conditions.
Thus, if there are any such abnormal or unusual shifts in the traffic pattern from the historic pattern(s) in either direction, i.e., early or late arrival of the passengers from the predicted conditions or events, the prior standard methodology could cause on these some few "abnormal" days the initiation of up-peak channeling at a time not in sync with the actual traffic pattern and/or maintain such up-peak channeling beyond the need for such channeling.
For example, if the system expected the arrival of a group of people at 8:10 AM, historic prediction would start anticipating and tuning the system to the expected destination of the people in the group. However, if this group of people were late for some reason (e.g., a traffic accident or other traffic delay, etc.), causing a temporal shift, to a later time, the system effectively would be "unaware" of the variance.
Hence, even though the real time prediction was then showing, for example, a traffic density of zero, the historic prediction factor would still affect the dynamic channeling to accommodate the historically expected group. However, since in fact there were no people to be served under the postulated conditions, the system operation under those circumstances would not be operating as effectively and efficiently as possible, and a later start of up-peak channeling would be desirable under these circumstances.
Additionally, at the other, terminating end of the channeling time spectrum, further inaccurate predictions could occur when a significant group of people would arrive early with respect to their normal (historic) arrival time. This would introduce another significant temporal shift in time (in this instance to an earlier time) of the real passenger traffic in comparison to the final prediction, when it was based in significant part on the historic factor.
For example, if people on, e.g., floor "ten" of a building historically came to work every day from 7:52 AM to 8:03 AM in the past, then the historic prediction factor would expect and predict the same behavior for today. However, if in fact some, relatively few people changed their habits, permanently or temporarily, then the equally weighted, final prediction methodology could again be out of sync. So, if every one on floor "ten" is on the job by, for example, 7:59 AM, the preferred, exemplary methodology of, for example, the '311 patent did not immediately detect the change(s) and would continue predicting and giving some weight to floor "ten" in continuing to creating dynamic channel(s), even though in fact there was then on that day no further need to do so for that floor. Thus, an earlier finish or end of the up-peak channeling operation would be desirable under these particular circumstances.
It should be noted that, although the '311 patent discussed initially using equally the two prediction factors, it also discussed varying the relative weights to be given to them over time based on the following methodology. As noted in the '311 patent, as a general statement, the relative values of the two prediction factors could be selected in a way which would cause the two types of predictors to be relatively weighted in favor of one or the other, or given equal weight if the "constants" are equal, as desired.
However, the relative values for "a" and "b" preferably were determined as follows. When the up-peak period started, the initial final predictions preferably assumed that a=b=0.5, namely the factors were at least initially to be treated equally. Further predictions were then made at the end of each minute, using the past several minutes data for the real time prediction, as well as using the historic prediction data.
The final predicted data for, for example, six (6) minutes was compared against the actual observations at those minutes. If at least, for example, four observations were either positive or negative and the error was more than, for example, twenty (20%) percent of the combined predictions, then the values of "a" and "b" were adjusted. This adjustment was preferably made using a "look-up" table generated, for example, based on past experience and experimentation in such situations. The look-up table provided relative values, so that, when the error was large, the real time predictions were given increasingly more weight.
An exemplary, typical look-up table suggested in the '311 patent is presented below:
______________________________________ VALUES For ERROR a b ______________________________________ 20% 0.40 0.60 30% 0.33 0.67 40% 0.25 0.75 50% 0.15 0.85 60% 0.00 1.00 ______________________________________
These values were further described as typically varying from building to building and could be "learned" by the system by experimenting with different values and comparing the resulting combined prediction against the actual, so that, for example, the sum of the square of the error was minimized. Thus, the prediction factors "a" and "b" were adaptively controlled or selected.
However, in the above, detailed, "look-up table" example of the preferred embodiment(s) of the '311 patent, if there was a significant late arrival of enough people that otherwise would have been sufficient with that day's actual beginning traffic to initiate up-peak channeling based on the historic data, up-peak channeling would be initiated and dynamic channeling assignments made to the cars for at least six (6) minutes, even though, for example, no significant traffic had yet arrived justifying the initiation of dynamic channeling operation of the elevator system.
This situation, which might be termed "late arrival" from the standpoint of the delayed arrival of the passengers causing the abnormal traffic shift or "too early start" from the standpoint of the pattern being designed to start based on the normal traffic flow, is graphically illustrated in FIG. 2A and could actually delay the service for at least some of the passengers that had in fact arrived, depending on their destination floors and the specific car assignments made as to the assigned floors in the dynamic channeling algorithm.
A like delay in response time for the proper termination of the up-peak channeling operation could occur, if, for example, most, if not substantially all, of the people going to one or more of the floors had in fact arrived earlier than historically had occurred in the past, resulting in these floors still being considered as having traffic to be accommodated under the dynamic channeling algorithm in use, when in fact such was not the case. This situation, which might be termed an "early finish" from the standpoint of the relatively early arrival of the passengers causing the abnormal traffic shift or a "too late finish" from the standpoint of the pattern being designed to end based on the normal traffic flow, is graphically illustrated in FIG. 2B and again would be less than ideal under these specific, relative unique, somewhat abnormal circumstances.
Thus, although, the adaptive approach of the invention of the '311 patent represented a very significant advance in the art, it did not cover all possible variances and in particular did not immediately adjust the initiation and termination of dynamic channeling to fit the currently existing traffic conditions, resulting in less than total optimization under these particular unusual variances.